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000863613 1001_ $$0P:(DE-HGF)0$$aPallast, Niklas$$b0
000863613 245__ $$aProcessing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri)
000863613 260__ $$aLausanne$$bFrontiers Research Foundation$$c2019
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000863613 520__ $$aMagnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.
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000863613 7001_ $$0P:(DE-HGF)0$$aDiedenhofen, Michael$$b1
000863613 7001_ $$0P:(DE-HGF)0$$aBlaschke, Stefan$$b2
000863613 7001_ $$0P:(DE-HGF)0$$aWieters, Frederique$$b3
000863613 7001_ $$0P:(DE-HGF)0$$aWiedermann, Dirk$$b4
000863613 7001_ $$0P:(DE-Juel1)176651$$aHoehn, Mathias$$b5$$ufzj
000863613 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon R.$$b6$$ufzj
000863613 7001_ $$0P:(DE-HGF)0$$aAswendt, Markus$$b7$$eCorresponding author
000863613 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2019.00042$$gVol. 13, p. 42$$p42$$tFrontiers in neuroinformatics$$v13$$x1662-5196$$y2019
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